Today, advanced data analysts make use of both predictive models and optimization problem solving to build data-driven decision making applications, a combination of technologies recently termed Prescriptive Analytics (PA). Current PA applications typically have multiple layers of poorly integrated components: a relational DBMS for data storage/management, ML tools for prediction, and specialized software packages for problem modeling and optimization problem solving. This complex stack leads to inefficient, labor-intensive, and error-prone PA workflows, blocking wider adoption of PA. In this paper, we present SolveDB+ -an RDBMS for PA applications which supports all PA steps with modeling, predictive, and optimization functionalities, and integrates these in a common SQL-based framework. Major SolveDB+ novelties are 1) a powerful SQL-based approach for PA problem specification and solving, 2) an extensible in-DBMS infrastructure for prediction and optimization solvers, and 3) in-DBMS modeling and management of PA models. SolveDB+ significantly improves both PA developer productivity and performance.

By expressing physical laws and control strategies, interoperable physical system models such as Functional Mock-up Units(FMUs) are playing a major role in designing, simulating, and evaluating complex (cyber-)physical systems. However, existing FMU simulation software environments require significant user/developer effort when such models need to be tightly integrated with actual data from a database and/or model simulation results need to be stored in a database, e.g., as a part of larger user analytical workflows. Hence, users encounter substantial complexity and overhead when using such physical models to solve analytical problems based on real data. To address this issue, this paper proposes pgFMU - an extension to the relational database management system PostgreSQL for integrating and conveniently using FMU-based physical models inside a database environment. pgFMU reduces the complexity in specifying (and executing) analytical workflows based on such simulation models(requiring on average 22x fewer code lines) while maintaining improved overall execution performance (up to 8.43x faster formulti-instance scenarios) due to the optimization techniques and integration between database and an FMU library. With pgFMU,cyber-physical data scientists are able to develop a typical FMUworkflow up to 11.74x faster than using the standard FMU software stack. When combined with an existing in-DBMS analytics tool, pgFMU can increase the accuracy of Machine Learning models by up to 21.1%.

In this demo, we present SolveDB - the first purely SQL-based DBMS for optimization problem solving and solver integration. SolveDB provides (1) an SQL-based syntax for optimization problem specification, (2) an extensible infrastructure of solvers for different classes of problems (e.g., linear programming), and (3) query optimization techniques to achieve the best execution performance and/or result quality. We demonstrate how our PostgreSQL-based SolveDB implementation allows simplifying specifications of data-driven optimization problems and increasing overall solving performance for (1) traditional well-known optimization problems, (2) "how-to" problems based on TPC-H, and (3) a complex energy planning problem with interlinked energy forecasting and load scheduling.

Many real-world decision problems involve solving optimization problems based on data in an SQL database. Traditionally, solving such problems requires combining a DBMS with optimization software packages for each required class of problems (e.g. linear and constraint programming) -- leading to workflows that are cumbersome, complex, inefficient, and error-prone. In this paper, we present SolveDB - a DBMS for optimization applications. SolveDB supports solvers for different problem classes and offers seamless data management and optimization problem solving in a pure SQL-based setting. This allows for much simpler and more effective solutions of database-based optimization problems. SolveDB is based on the 3-level ANSI/SPARC architecture and allows formulating, solving, and analysing solutions of optimization problems using a single so-called solve query. SolveDB provides (1) an SQL-based syntax for optimization problems, (2) an extensible infrastructure for integrating different solvers, and (3) query optimization techniques to achieve the best execution performance and/or result quality. Extensive experiments with the PostgreSQL-based implementation show that SolveDB is a versatile tool offering much higher developer productivity and order of magnitude better performance for specification-complex and data-intensive problems.

In many scientific and commercial domains, we encounter flexibility objects, i.e., objects with explicit flexibilities in a time and an amount dimension (e.g., energy or product amount). Applications of flexibility objects require novel and efficient techniques capable of handling large amounts of such objects while preserving flexibility. Hence, this paper formally defines the concept of flexibility objects (flex-objects) and provides a novel and efficient solution for aggregating and disaggregating flex-objects. Out of the broad range of possible applications, this paper will focus on smart grid energy data management and discuss strategies for aggregation and disaggregation of flex-objects while retaining flexibility. This paperfurther extends these approaches beyond flex-objects originating from energy consumption by additionally considering flex-objects originating from energy production and aiming at energy balancing during aggregation. In more detail, this paper considers the complete life cycle of flex-objects: aggregation, disaggregation, associated requirements, efficient incremental computation, and balance aggregation techniques. Extensive experiments based on real-world data from the energy domain show that the proposed solutions provide good performance while satisfying the strict requirements.

Industrial processes use energy to transform raw materials and intermediate goods into final products. Many efforts have been done on the minimization of energy costs in industrial plants. Apart from working on “how” an industrial process is implemented, it is possible to reduce the energy costs by focusing on “when” it is performed. Although, some manufacturing plants (e.g. refining or petrochemical plants) can be inflexible with respect to time due to interdependencies in processes that must be respected for performance and safety reasons, there are other industrial segments, such as alumina plants or discrete manufacturing, with more degrees of flexibility. These manufacturing plants can consider a more flexible scheduling of the most energy-intensive processes in response to dynamic prices and overall condition of the electricity market. In this scenario, requests for energy can be encoded by means of a formal structure called flex-offers, then aggregated (joining several flex-offers into a bigger one) and sent to the market, scheduled, disaggregated and transformed into consumption plans, and eventually, into production schedules for given industrial plant. In this paper, we describe the flex-offer concept and how it can be applied to industrial and home automation scenarios. The architecture proposed in this paper aims to be adaptable to multiples scenarios (industrial, home and building automation, etc.), thus providing the foundations for different concept implementations using multiple technologies or supporting various kinds of devices.

A privacy-aware proximity detection service determines if two mobile users are close to each other without requiring them to disclose their exact locations. Existing proposals for such services provide weak privacy, give low accuracy guarantees, incur high communication costs, or lack flexibility in user preferences. We address these shortcomings with a client-server solution for proximity detection, based on encrypted, multi-level partitions of the spatial domain. Our service notifies a user if any friend users enter the user's specified area of interest, called the vicinity region. This region, in contrast to related work, can be of any shape and can be flexibly changed on the fly. Encryption and blind evaluation on the server ensures strong privacy, while low communication costs are achieved by an adaptive location-update policy. Experimental results show that the flexible functionality of the proposed solution is provided with low communication cost.